System and Method for Determining a Location Area of a Mobile User

ABSTRACT

A computer system determines location probabilities for defined sub-areas (41, 43) of a geographical area (4), e.g. for squares arranged in a grid (40), based on field strengths expected in the small sub-areas (41, 43) from the antennas (A1, A2 A3, A4), and based on the antenna used by the mobile communication terminal. The location probabilities indicate for a sub-area (41, 43) the probability that the mobile communication terminal is located in the respective sub-area (41, 43). The computer system determines at least one ellipse defining the location area, based on the location probabilities of the sub-areas (41, 43). The location probabilities make it possible to determine elliptical location areas of mobile users much smaller than the area each antenna does cover physically, but without the need for measuring at the mobile communication terminal signal properties such as signal strength or observed time differences.

FIELD OF THE INVENTION

The present invention relates to a system and a method for determining alocation area of a mobile user. Specifically, the present inventionrelates to a computer system and a computer-implemented method fordetermining a location area of a user using a mobile communicationterminal in a geographical area covered by a mobile radio network.

BACKGROUND OF THE INVENTION

For various location based services as well as for handling emergencysituations, it is essential to determine as accurately as possible thegeographical location of a user of a mobile communication terminal.Mobile communication terminals include, for example, mobile radio(cellular) telephones or personal digital assistants (PDA) as well asother portable computers with communication modules for mobile radionetworks, such as GSM (Global System for Mobile Communication) or UMTS(Universal Mobile Telecommunication System). Although there are mobilecommunication terminals available which include a GPS-receiver (GlobalPositioning System) or another satellite-based positioning system, thereis still a need for other location methods, as for example locatingmobile communication terminals which are not equipped with suchpositioning systems, mobile communication terminals which have their GPSturned off or inside of buildings where the GPS signal is too weak. Itis known from the mobile network which antenna the user is using.However, particularly in rural areas, an area served by an antenna cancover a very large geographical area. Unfortunately today calculationsof these areas are not accurate, often they are too large or they arenot reliable, and in reality mobile communication terminals are oftenlocated outside of these areas (low hit rate).

It is essential to determine smaller and more reliable location areas ofmobile users in geographical areas covered by mobile radio networks.Particularly, there are legal regulations which require the geographicallocations of users of mobile communication terminals to be provided asshapes which define a geographical area where the mobile user is mostlikely located. These shapes are required to be described in a simpleand straightforward geometric fashion, e.g. In the form of polygons, acircle or one or more ellipses.

GB 2352134 describes a method of locating a mobile telephone based on acalculation of expected signal properties such as signal strength orobserved time differences for a plurality of possible locations, e.g.arranged in a grid. The expected signal property is compared to ameasured signal property. Based on the comparison, determined is theprobability that the mobile telephone is at one or more of thelocations. Thus, the method of GB 2352134 is based on the actual valuesof the field strength or time differences measured at the mobiletelephone. However, these values would have to be transmitted from themobile telephones to a centralized measuring system and are thereforenot necessarily available for locating a mobile telephone. Furthermore,manufacturers of proprietary network components do not necessarily makesuch values available to the operators of mobile networks or they sellthem at considerable cost. The method does also fall when there are lessthan 3 antennas available, or if the visible antennas are arranged alonga line, for example, in mountain areas.

Patent application WO 99/07177 describes a method of determining thelocation of a mobile communication terminal using elliptical positionestimates as an improvement over circular estimates. However, theteachings of WO 99/07177 are limited to defining an elliptical positionestimate based on 1000 simulated locations which form a substantiallysymmetrical and elliptical statistical sample. Typically, however,measurement data from real networks do not provide symmetrical andelliptical clusters of possible locations of a mobile user. The locationareas do have in reality very different shapes, depending for example onthe position of other antennas or the elevation model of thegeographical area, for example hills and valleys. Furthermore, whenpolygons or ellipses are created around estimated locations, they cancover very large areas, because just one single estimated locationpoint, far away from all other location points, can possibly enlarge thepolygons or ellipses more than 100 times. Thus, for real life scenariosit is necessary to reduce the hit rate and therefore find polygons orellipses which cover much smaller location areas, in fact as small aspossible, but still with a satisfactory hit rate (probability) of over95%, for example.

SUMMARY OF THE INVENTION

It is an object of this invention to provide a system and a method fordetermining a geographical location area of a mobile user. Inparticular, it is an object of this invention to provide a system and amethod for determining one or more elliptical location areas of a userusing a mobile communication terminal in a geographical area covered bythe mobile radio network.

According to the present invention, these objects are achievedparticularly through the features of the independent claims. Inaddition, further advantageous embodiments follow from the dependentclaims and the description.

According to the present invention, the above-mentioned objects areparticularly achieved in that, for determining a location area of a userusing a mobile communication terminal in a geographical area covered bya mobile radio network, the geographical area is divided into aplurality of sub-areas. For example, the geographical area is dividedinto sub-areas of equal shape and size, having a diameter in the rangeof 50 to 150 meters. For example, the sub-areas are squares arranged ina grid, or hexagons arranged in a comb structure. Based on fieldstrengths expected in the sub-areas for antennas located in thegeographical area, and based on the antenna used by the mobilecommunication terminal, location probabilities are determined for thesub-areas. The location probabilities indicate the probability that themobile communication terminal is located in the respective sub-area.Preferably, the location probabilities are determined based on antennaprobabilities associated with the sub-areas. The antenna probabilitiesindicate for at least some of the antennas, the probability that themobile communication terminal, when located in a particular sub-area,uses the respective antenna. The antenna probabilities are determinedbased on the field strengths expected in the sub-areas for therespective antennas. Specifically, the antenna probabilities aredetermined from the distribution of field strengths in the sub-areas forthe respective antennas, combined with the knowledge that the mobilecommunication terminal typically uses the antenna with the highest realfield strength at each location of the sub-area. The resulting antennaprobabilities do have a high reliability, as the uncertainty of theprecision of the prediction of the field strength is statistically fullytaken into account in the antenna probability calculation. Subsequently,based on the location probabilities of the sub-areas, determined is atleast one ellipse defining the location area. Determining an ellipticlocation area based on the location probabilities of the sub-areas makesit possible to provide information about the location of the mobilecommunication terminal in the geographical area based on the antennaused, with elliptical location areas of mobile users much smaller thanthe area each antenna covers physically, but without the need formeasuring at the mobile communication terminal signal properties such assignal strength or observed time differences, and without therequirement to have everywhere coverage of at least three antennas notarranged along a line.

In a first approach, the ellipse is determined by defining a singleellipse around a polygon enveloping probable sub-areas having at least adefined minimum location probability. The location area is defined bythe single ellipse, if the number of the probable sub-areas is lowerthan a defined maximum number of sub-areas, e.g. less than two hundredsub-areas, and a density of the probable sub-areas is higher than adefined minimum density, i.e. If there are a sufficient number ofsub-areas with a high location probability included in the ellipse. Forexample, the density is defined by the ratio of the number of probablesub-areas included in the ellipse to the number of total sub-areasconsidered in the ellipse. If the density of probable sub-areas is notsufficient, more than one ellipse is determined for defining thelocation area.

For determining more than one ellipse, in a second approach, determinedare sub-areas to be considered by selecting from the sub-areas withdescending location probability those sub-areas which together make upat least a defined target probability. It should be noted that prior toselecting the sub-areas to be considered, a smoothing filter is appliedto the location probabilities of the sub-areas, smoothing out the valuesof the location probabilities. Subsequently, the sub-areas to beconsidered are grouped into areas of connected sub-areas, and to definethe location area an ellipse is determined around each of the areas ofconnected sub-areas. For example, an area of connected sub-areas isdetermined by selecting with descending location probability from thesub-areas to be considered sub-areas which share a common border with asub-area already included in the respective area of connected sub-areas.Selected sub-areas are removed from the sub-areas to be considered. Anarea of connected sub-areas is started with the sub-area of theremaining sub-areas to be considered having the highest locationprobability.

In an embodiment, the target probability is increased, if the number ofellipses obtained exceeds a defined maximum number of ellipses, anddetermination of more than one ellipse is repeated using the increasedtarget probability.

For determining more than one ellipse, in a third approach, an ellipseis enlarged using those sub-areas closest to the ellipse which have anintermediate area with at least a defined minimum mean locationprobability. New ellipses are generated at sub-areas where the meanlocation probability of the intermediate area is below the minimum meanlocation probability, if the maximum number of ellipses has not beenreached. Preferably, the intermediate area is a geometric object, placedbetween the sub-area in question and the ellipse closest to the sub-areain question. For example, the intermediate area is a circle.

In an embodiment, the defined minimum mean location probability islowered, if the defined target probability has not been reached, and thedetermination of more than one ellipse is repeated using the loweredminimum mean location probability.

In addition to a computer system and a computer-implemented method fordetermining a location area of a user using a mobile communicationterminal in a geographical area covered by a mobile radio network, thepresent invention also relates to a computer program product includingcomputer program code means for controlling one or more processors of acomputer system, particularly, a computer program product including acomputer readable medium containing therein the computer program codemeans.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be explained in more detail, by way ofexample, with reference to the drawings in which:

FIG. 1 shows a block diagram illustrating schematically an exemplaryconfiguration of a system for locating a mobile communication terminalassociated with a mobile radio network.

FIG. 2 shows a flow diagram illustrating an example of a sequence ofsteps executed for locating the mobile communication terminal in ageographical area.

FIG. 3 shows a block diagram illustrating schematically sub-areas of apartial region of a geographical area covered by antennas of the mobileradio network.

FIG. 4 shows a graph illustrating exemplary distributions of the fieldstrengths expected in a sub-area from two different antennas.

FIG. 5 shows a flow diagram illustrating an example of a sequence ofsteps executed for determining a locating area of the mobilecommunication terminal in the geographical area.

FIG. 6 shows a diagram illustrating a single ellipse around a polygonenveloping probable sub-areas.

FIG. 7 shows a diagram illustrating more than one ellipse, and anintermediate area separating a sub-area from its closest ellipse.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In FIG. 1, reference numeral 1 refers to a mobile communication terminalsuch as a mobile radio (cellular) telephone, a PDA or another portablecomputer. The mobile communication terminal 1 comprises a communicationmodule for communicating (voice and/or data) via mobile radio network 2,e.g. a GSM or UMTS network or another cellular radio network. Asillustrated schematically in FIG. 3, the cellular network comprises aplurality of antennas A1, A2, A3, A4, each covering a more or lessoverlapping area C1, C2, C3, C4 of the geographical area 4. Each antennaA1, A2, A3, A4 is controlled by a base station connected to a mobileswitching center (e.g. MSC) of the mobile radio network 2. The antennasA1, A2, A3, A4 are identified by their identification id in the network,which correspond to the areas C1, C2, C3, C4.

In FIG. 1, reference numeral 3 refers to a computer system connected tothe mobile radio network 2. Computer system 3 includes one or morecomputers, for example personal computers or servers, comprising one ormore processors. Computer system 3 further comprises at least one dataentry and display terminal 38 connected to at least one of itscomputers. Furthermore, computer system 3 comprises a data store 30,e.g. a database and/or one or more data files and various functionalmodules namely a sub-area definition module 31, a field strengthprediction module 32, an antenna probability calculation module 33, anantenna determination module 34, a location probability calculationmodule 35, a location determination module 36, and a location areadetermination module 37. Preferably, the functional modules and the datastore 30 are implemented as programmed software modules. The computerprogram code of the software modules is stored in a computer programproduct, i.e. In a computer readable medium, either in memory integratedin a computer of computer system 3 or on a data carrier which can beinserted into a computer of computer system 3. The computer program codeof the software modules controls the computer(s) of computer system 3 sothat the computer system 3 executes various functions described in thefollowing paragraphs with reference to FIGS. 2 to 4.

As illustrated schematically in FIG. 2, computer system 3 is configuredto perform preparatory step S1 for generating, for all sub-areas 41, 43,antenna probabilities which indicate for each sub-area the probability amobile communication terminal 1 located in this sub-area uses therespective antenna A1, A2, A3, A4 for registering and/or communicatingwith the mobile radio network 2. Preparatory step S1 is performedperiodically, e.g. monthly, and/or whenever there is a significantchange in the radio network, e.g. If an antenna A1, A2, A3, A4 is turnedoff, a new antenna is added or settings of an antenna are altered.

Furthermore, computer system 3 is configured to perform step S2 forlocating the mobile communication terminal 1 in the geographical area 4.Step S2 is performed as requested by a user of data entry terminal 3, acontrol application running on computer system 3, or a location basedservice application running on computer system 3 or a remote computersystem.

In step S11, sub-area definition module 31 divides the geographical area4 into a plurality of sub-areas 41, 43, as illustrated schematically inFIG. 3. In the example of FIG. 3, the geographical area 4 is dividedinto a grid 40 of equal-sized squares, each square defining a sub-area41, 43. For example, the sub-areas 41, 43 are squares of 100 m×100 m.One skilled in the art will understand that alternative shapes ofsub-areas are possible, for example, the geographical area 4 may bedivided into hexagons arranged in a comb structure. Furthermore, it isalso possible to have various sizes of sub-areas, for example smallersub-areas may be used in zones of increased interest and/or population.Typically, a sub-area is defined by a unique identifier and one or moreparameters which describe direct or indirect the position coordinates.Depending on the embodiment, a sub-area is further defined by sub-areatype, size and/or shape information. Thus, sub-area definition module 31defines and stores in the data store 30 a list or array comprising thedefined sub-areas 41, 43 of the geographical area 4. In an embodiment,the sub-area definition module 31 is configured to read the definitionof the sub-areas from a data file.

In an embodiment, the sub-area definition module 31 is configured tosupport manual or file based entry of the antenna probability ofantennas, which would require in the following steps a full 3D modelingof radio propagation, taking into account the exact position of theantenna inside of a 3D physical environment, as for example an enclosedspace such as in a tunnel, or on different floors in shops or railwaystations. As there are not many of these enclosed antennas and thepropagation of the radiation does usually follow the physical shape ofthe environment where they are installed, the antenna probability of theenclosed antennas are manually acquired based on maps where theseantennas are located and loaded into the data store 30. In an embodimentshapes assigned to enclosed antennas are loaded into the data store 30and all antenna probabilities are set to 100% when the sub-area isinside an enclosed space including an enclosed antenna.

In step S12, field strength prediction module 32 calculates, for all thesub-areas 41, 43 defined for the geographical area 4, the fieldstrengths expected in the respective sub-area 41, 43 from the antennasA1, A2, A3, A4 of the mobile radio network 2, considering data abouttopography and power characteristics associated with the antennas A1,A2, A3, A4. Field strength prediction modules 32 are availablecommercially, e.g. offered by Aricom International. Preferably, only theantennas A1, A2, A3, A4 having the highest expected field strengthvalues are stored for a sub-area 41, 43 in data store 30. For example,the field strength values are stored for a defined (configurable) numberof the strongest antennas A1, A2, A3, A4, e.g. for the seven or fourteenstrongest antennas. Table 1 shows exemplary entries of expected fieldstrengths in data store 30. In the example of Table 1, a field strengthof −41 dBm is expected in sub-area 41 for antenna A4, whereas a fieldstrength of −52 dBm is expected in the same sub-area for antenna A1;likewise, in sub-area 43, a field strength of −42 dBm is expected forantenna A3, whereas a field strength of −48 dBm is expected for antennaA4. In addition or as an alternative to a unique identifier, antennadata may include antenna coordinates or grid positions, for example.Moreover, in addition to the field strength value, further fieldstrength data may include standard deviations for the expected fieldstrength distribution of the antenna in the respective sub-area. Thus,the field strength for a given sub-area i is preferably modeled as arandom variable having a normal distribution with expected value d_(i)and standard deviation s_(i). Modeling the field strength as adistribution is necessary because the real field strengths do have bigvariations, depending on the exact position inside the sub-area,location of buildings, weather conditions, how the mobile communicationterminal is oriented in space and many other factors. Instead ofconsidering all these factors into the model calculation, the resultingnormal distribution can be used and its standard deviation measured infield tests where the real field strengths are compared with expectedfield strengths for many test calls.

TABLE 1 expected field strength sub-area antenna field further fieldsub-area sub-area antenna antenna strength strength identifier dataidentifier data value data . . . . . . . . . . . . . . . . . . 41 . . .A4 . . . −41 dBm . . . 41 . . . A1 . . . −52 dBm . . . 41 . . . A3 . . .−55 dBm . . . . . . . . . . . . . . . . . . . . . 43 . . . A3 . . . −42dBm . . . 43 . . . A4 . . . −48 dBm . . . 43 . . . A2 . . . −51 dBm . .. . . . . . . . . . . . . . . . . . .

In step S13, antenna probability calculation module 33 determines foreach sub-area 41, 43 the antenna probabilities for the antennas A1, A2,A3, A4.

In sub-step S131, based on the expected field strength values stored inthe database or collection of files 30, antenna probability calculationmodule 33 calculates for each sub-area 41, 43 the antenna probabilities,i.e. the probability in the respective sub-area 41, 43, the antenna A1,A2, A3, A4 is being used by a mobile communication terminal 1 forregistering and/or communicating with mobile radio network 2, asoutlined below.

In FIG. 4, x₁ refers to the expected field strength (here −41 dBm) of anantenna A1 at a given location, i.e. In a given sub-area 41, 43.Reference numeral D₁ refers to the distribution of the real fieldstrength which can be measured at this sub-area with a mobilecommunication terminal. In this sample it is likely that the real fieldstrength will be around −41 dBm, but sometimes the field strength is −45dBm, sometimes −37 dBm, it will change depending on the precise locationwithin the sub-area 41, 43, e.g. within the 100 m×100 m square, theweather, the type of terminal, how the terminal is oriented in space,etc. Likewise, x₂ refers to the expected field strength (here −55 dBm)of another antenna A2 in the same sub-area 41, 43, and reference numeralD₂ refers to the distribution of the real field strength of antenna A2.For calculating the probability that the mobile communication terminal 1will use antenna A1 in the respective sub-area 41, 43, distribution D₁of the real field strength of antenna A1 is divided into small sectors,such as sector S showing the probability that the real field strength ofantenna A1 is in a small range between −40 dBm and −41 dBm. Forcalculating the probability the real field strength of antenna A1 isbetween −40 dBm and −41 dBm, and higher than the real field strength ofantenna A2 (resulting in the use of antenna A1), the following twoprobabilities are used:

-   -   1) Probability that the real field strength of antenna A1 is        between −40 dBm and −41 dBm, which corresponds to the area of        section S between −40 dBm and −41 dBm; and    -   2) Probability that the real field strength of antenna A2 will        be less than −41 dBm, which corresponds to area F below −41 dBm.

As probabilities 1) and 2) are independent, the probability that bothconditions are met at the same time can be calculated by multiplying theprobabilities 1) and 2) (product of areas S and F).

The above calculated probability is restricted to the case where antennaA1 is between −40 dBm and −41 dBm. For calculating the probability 3)for all real fields strengths of antenna A1, the limit is calculated forall summed products of S and F for all real field strengths between−infinity to +infinity, while letting the width of the sector S go tozero. This results in the following integral:

$p_{A\; 1} = {\overset{\infty}{\int\limits_{- \infty}}{{N\left( {x,d_{A\; 1},s_{A\; 1}} \right)} \cdot {F\left( {x,d_{A\; 2},s_{A\; 2}} \right)} \cdot {dx}}}$

N(x, d_(A1), s_(A1)) is the normal distribution of the real fieldstrength x of antenna A1, with expected power level d_(A1) and standarddeviation s_(A1):

${N\left( {x,d_{A\; 1},s_{A\; 1}} \right)} = {\frac{1}{s_{A\; 1}\sqrt{2\pi}}e^{{- \frac{1}{2}}{(\frac{x - d_{A\; 1}}{S_{A\; 1}})}^{2}}}$

F(x,d_(A2),s_(A2)) is the probability the real field strength y of theantenna A2 with expected power level d_(A2) and standard deviations_(i)s lower than x:

F(x,d _(A2) ,s _(A2))=∫_(−∞) ^(x) N(y,d _(A2) ,s _(A2))dy

For example, F(−60,−40,5) is the probability the real field strengthwill be between −infinity and −60, for a normal distribution at x=−40and a standard deviation of 5.

Taking into account another antenna A3 is straightforward, as it isanother independent condition which has to be met, so the antenna A1 isstill used rather than the antennas A2 or A3. So the probability 3)outlined above can be extended with this additional condition, which isthe probability that the field strength of antenna A3 is smaller than x.The resulting probability 4) is:

$p_{A\; 1} = {\overset{\infty}{\int\limits_{- \infty}}{{N\left( {x,d_{A\; 1},s_{A\; 1}} \right)} \cdot {F\left( {x,d_{A\; 2},s_{A\; 2}} \right)} \cdot {F\left( {x,d_{A\; 3},s_{A\; 3}} \right)} \cdot {dx}}}$

More antennas are taken into account the same way as antennas A2 and A3.In addition the variable substitution d_(A1)< >d_(Ai) and s_(A1)< >

does allow to calculate the probability the call will go to one of theother antennas A_(i). This does allow rephrasing the calculation 4) in ageneral way. So the antenna probability p_(Ai) 5) that the mobilecommunication terminal will connect to a given antenna A_(i) with anexpected power level d_(Ai) and standard deviation s_(Ai) is given by:

$\mspace{79mu} {{p\text{?}} = {\overset{\infty}{\int\limits_{- \infty}}{{N\left( {x,{d\text{?}},{s\text{?}}} \right)} \cdot {\overset{n}{\prod\limits_{{j = 1},{j \neq 1}}}{{F\left( {x,d_{Aj},s_{Aj}} \right)} \cdot {dx}}}}}}$?indicates text missing or illegible when filed

In an embodiment, for each network type the same standard deviation isused for s_(Ai). For GMS networks, a deviation of 10 is used, for UMTSnetworks a deviation of 4. In a further embodiment, higher deviationsare used with increased distance of a sub-area 41, 43 from therespective antenna A1, A2, A3, A4. For example, a standard deviation ofapproximately 8 is used for UMTS networks, if the antenna is locatedfarther than 5 km from the respective sub-area.

As the mobile communication terminals 1 cannot make a call-setup, if thefield strength is below a certain value, the lower limit in the formulasoutlined above is adjusted from −infinity to a defined (configurable)minimum field strength, depending on the network type of the cell beingGSM or UMTS, for example. Furthermore, as the mobile communicationterminal 1 and the mobile radio network 2 do not distinguish in theantenna selection field strength values higher than a defined(configurable) value, e.g. −40 dBm, all expected field strength valuesabove this value are limited to this value.

The integral cannot be solved algebraically. As the input data isimprecise, it is not necessary to have an infinite precision in theprobability calculation. So N(x, d_(i), s_(i)) can be approximated by 0in the regions of x≤d_(i)−5s_(i) and x≥d_(i)+5s_(i). Which is the sameas when the boundaries]−∞,∞*[ are replaced by [d_(i)−5s_(i),d_(i)+5s_(i)]. In a next approximation step the interval is divided intoa finite number of segments, e.g. 10 or 20, for numerical integrationwith the Simpson's method. For speeding up the automatic computation,values of N(x_(k), 0, 1) and F(y_(k), 0, 1) can be precomputed andstored for different values of x_(k) or y_(k), respectively.

In step S132, antenna probability calculation module 33 sets the antennaprobabilities of enclosed antennas to the values loaded in step S11 intodata store 30. In an embodiment all antenna probabilities are set to100%, which are inside enclosed spaces assigned to enclosed antennas,which were loaded before in step S11 into data store 30.

In step S133, antenna probability calculation module 33 takes intoaccount mobile communication terminals, configured to switchautomatically between different networks as for example GSM or UMTS.This does affect the location area. The implementation of antennaprobability calculation module 33 depends on how multiband mobilecommunication terminals select the network type. This has to beestimated in the field or in a lab using multiband terminals withdifferent signal levels from multiple networks. In an embodiment ofantenna probability calculation module 33, for GSM and UMTS, the antennaprobabilities are calculated separate for each network, as according tothe measurements multiband mobile communication terminals always preferUMTS whenever available. This way no terminal detection is necessary. Itdoes only have the disadvantage that GSM areas could be smaller formultiband mobile communication terminals in GSM areas, where there isUMTS coverage as long as UMTS is not turned off on the mobilecommunication terminal. In an embodiment, it is possible to use a mobilecommunication terminal detection and calculate the GSM areas formultiband mobile communication terminals separately; setting on eachsub-area the antenna probability of all GSM antennas to zero if there isan UMTS antenna available with more than a defined (configurable)minimum field strength.

The antenna probabilities resulting from step S13 are stored temporarilyfor each antenna in the memory of the computer system 3 or in data store30. For example, the antenna probabilities are stored for a defined(configurable) number of the strongest antennas A1, A2, A3, A4, e.g. forthe seven or fourteen strongest antennas. Table 2 shows exemplaryentries of calculated antenna probabilities in data store 30. In theexample of Table 2, for sub-area 41, it is expected that with aprobability of 50% antenna A4 will be used, whereas the probability ofantenna A1 is 25%; likewise, for sub-area 43, a probability of 40% isexpected for antenna A3, whereas a probability of 35% is expected forantenna A4. One skilled in the art will understand that Tables 1 and 2may be combined in one or more files or in a common table.

TABLE 2 sub-area antenna sub-area sub-area antenna antenna antennaidentifier data identifier data probability . . . . . . . . . . . . . .. 41 . . . A4 . . . 50% 41 . . . A1 . . . 25% 41 . . . A3 . . . 15% . .. . . . . . . . . . . . . 43 . . . A3 . . . 40% 43 . . . A4 . . . 35% 43. . . A2 . . . 10% . . . . . . . . . . . . . . .

In step S21, antenna determination module 34 determines for a particularmobile communication terminal 1 the antenna A1, A2, A3, A4 used, i.e.the antenna currently or last used, from identification data provided bythe mobile radio network 2, e.g. cell identifier or base stationidentifier. In different embodiments and/or applications, thisinformation is obtained by antenna determination module 34 from the MSCof the mobile radio network 2, the Home Location Register (HLR)associated with the mobile communication terminal 1, or the VisitorLocation Register (VLR) or another network component of the mobile radionetwork 2. In a further embodiment, antenna determination module 34 isconfigured to send a message to the mobile communication terminal 1,e.g. an (invisible) SMS (Short Messaging Services) or USSD (UnstructuredSupplementary Service Data) message, to trigger the mobile communicationterminal 1 to use an antenna A1, A2, A3, A4 from the current locationand, thus, update the respective identification information in themobile radio network 2. In a further embodiment, the antennadetermination module 34 is configured to receive identificationinformation for defining the antenna used by the user or the mobilecommunication terminal 1, respectively, from an operator or a softwareapplication, for example.

In step S22, location probability calculation module 35 calculates foreach sub-area of an antenna a location probability, which is theprobability that the user is in the respective sub-area.

In an embodiment location probability calculation module 35 firstreduces the size of the user location areas using additional parametersfrom the network, obtained by antenna determination module 34, forexample TA (timing advance) or RTT (round trip time). These parameterscan be used to indicate the probability the sub-area does have therespective parameter, depending on the distance between the sub-area andantenna. Field tests are made in advance to estimate the distribution ofdistance for each possible value of one of these parameters. If,depending on the network 2, antenna determination module 34 is able todeliver such a parameter, for example for a call timing advance of four(4), it is possible to calculate the probability that both will happen:the mobile communication terminal 1 selects at a sub-area a givenantenna AND the network parameter from antenna determination module 34is four (4). As both conditions are independent the combined probabilitycan be calculated multiplying the antenna probability and theprobability for the parameter from antenna determination module 34 beingfour (4). The resulting combined probability is used in the followingsteps as an improvement for the antenna probability calculated before.

The sum of all location probabilities of an antenna is one (1), as it isknown from the network which antenna is used. This allows calculatingthe distribution of location probability P_(U) in all sub-areas from theantenna probability P_(Ai) calculated in the steps before and the totalantenna probability of all n sub-areas:

$\mspace{79mu} {{{P\text{?}} = \frac{P\text{?}}{\sum\limits_{n}P_{Aj}}},{\text{?}\text{indicates text missing or illegible when filed}}}$

The calculated location probabilities are calculated and stored for eachantenna and its sub-areas 41, 43 in data store 30. It must be noted thatone antenna can have one or more sub-areas 41, 43 with a locationprobability >0, and one sub-area 41, 43 can have more than one antennawith a location probability >0.

In an embodiment, location probability calculation module 35 reduces thesize of areas with a minimal antenna probability using a sequence ofhistorical location determinations, calculating for each locationdetermination the location probability as in the steps before,calculating for each location determination the time until the lastlocation determination, calculating for each location determination themaximum distance the user can have traveled in all directions andspreading for each location determination all probabilities of therespective sub-area in the range between zero and the max distance. Theresulting location probabilities for each location determination arethen blended multiplying the resulting location probabilities. Theresulting combined location probabilities are used in the followingsteps as an improvement for the location probabilities calculatedbefore.

In step S23, location determination module 36 optimizes the locationprobabilities, correcting in this last step errors which are caused bylimitations of the granularity of the grid or input data used forcalculating the field strength predictions. In one embodiment, tocorrect for errors caused by the limitation of the granularity of thegrid or comb structure, for each antenna all location probabilities >0are expanded at the border twice the size of the grid, e.g. 200 meters.In another embodiment, the location determination module 36 is furtherconfigured to show the resulting location probabilities for each antennaA1, A2, A3, A4 graphically on a display of data entry terminal 38, thesub-areas 41, 43 having a location probabllity>0, for example colorcoded in a way a high value of the location probability correlate withcolor schemas as for example black/grey/white or different shades of oneor more colors like, for example, white, blue and red. In yet anotherembodiment, e.g. In order to adhere to government regulations, generatedand displayed is a location area, based on the determined locationprobabilities, for example an elliptical location area, representativeof the geographical area where the mobile communication terminal 1 isexpected to be located when the respective antenna was used by themobile communication terminal 1. In a further embodiment, computersystem 3 comprises a communication module configured to transmit thedetermined location probabilities, a graphic representation of thelocation probabilities and/or the (elliptical) location area to a mobilecommunication terminal 1. In this further embodiment, the mobilecommunication terminal 1 is configured to show the received locationprobabilities and/or location area on a map, e.g. using geographicalinformation services such as Google Maps by Google Inc.

As illustrated schematically in FIG. 5, computer system 3 is configuredto determine in step S5 an estimated location area of a user using themobile communication terminal 1 in the geographical area 4 covered bythe mobile radio network 2.

In step S3, computer system 3 divides the geographical area 4 into aplurality of sub-areas 41, 43, as described above in the context of stepS11.

In step S4, based on the field strengths expected in the sub-areas 41,43 for the antennas A1, A2, A3, A4 located in the geographical area 4,and based on the antenna used by the mobile communication terminal 1,the computer system 3 determines for the sub-areas 41, 43 locationprobabilities, indicative of the probability that the mobilecommunication terminal 1 is located in the respective sub-area 41, 43,as described above in the context of steps S1, S2 and particularly S22.

In step S5, based on the calculated location probabilities of thesub-areas 41, 43, the location area determination module 37 determinesat least one ellipse defining the location area for each antenna.

In step S50, the location area determination module 37 first checks ifthere are only few sub-areas. In this case, small changes in the inputvalues which are used for the field strength calculation do result invery different ellipses. These kinds of ellipses cannot be used inpractice. One of the possible solutions is to select all sub-areas whichdo have at least a defined (configurable) minimum location probabilityand draw an ellipse around all of them. It will result in ellipses thatare possibly too large, but this is unavoidable, as the limitations ofthe grid and the input data for the grid do not allow calculatingellipses so small. So the location area determination module 37 firstchecks if there are not more than a defined (configurable) maximumnumber of sub-areas, e.g. 250, which do have at least a defined(configurable) minimum location probability. If the maximum number hasnot been exceeded, which is often related to small “clouds” (clusters)of sub-areas, the method continues in step S51; otherwise, the methodcontinues in step S53.

In step S51, the location area determination module 37 defines a singleellipse 5 fitting around a polygon 6 enveloping probable sub-areashaving at least the defined (configurable) minimum location probability.

In step S52, the location area determination module 37 checks thedensity of these sub-areas inside the ellipse. If the density is higherthan a defined (configurable) minimum density, the method continues instep S62; otherwise, the method continues in step S53.

In step S53, the location area determination module 37 tries to use morethan one ellipse for a description of the location probability of anantenna. The main idea in this step is to group connected sub-areasaround “seeds” with high probability. As the probabilities between twoadjacent sub-areas do often change significantly and the followingmethod is very dependent on the change of probability between adjacentsub-areas, the location probabilities are first smoothed for this stepby a defined (configurable) factor. For example, the locationprobabilities are smoothed out using a smoothing filter as known forexample in digital image processing. The sub-areas 41, 43 are thensorted in a list according to their location probabilities. Startingwith the first sub-area in the list and in descending order of theirlocation probabilities, sub-areas are selected in a list for furtherprocessing until the sum of their location probabilities reaches adefined (configurable) minimum target probability, e.g. 25%. All othersub-areas are not processed for ellipse creation any more. Starting withthe first sub-area in the list, all those sub-areas from the list areselected for the first cloud of sub-areas, which share a common borderwith sub-areas already included in the first cloud. The method is thenrepeated for all other non selected clouds of sub-areas, starting forthe next cloud with the first non selected sub-area from the list. StepS53 ends when all sub-areas from the list have been assigned to a cloud.To avoid too many clouds or clouds that are too small, the creation ofclouds is stopped if the number of sub-areas included in a cloud meets astopping criteria, e.g. If the number of sub-areas in a new cloud isbelow 10% of the sub-areas belonging to the first cloud. Subsequently,for each cloud an ellipse 5 is fitted around polygon 6 enveloping allsub-areas of a cloud.

With exception of small ellipses, the resulting ellipses are usually toobig, as they do always include all sub-areas of a cloud, covering a lotof empty space. It is possible to reduce the size of these ellipses,resizing them around areas with high density of sub-areas. In anembodiment, the optimization is based on three steps:

-   a) The length of both the long and the short axis of the ellipse 5    are extended/shortened in a linear fashion, until the area of the    ellipse 5 is equal to the area of the polygon 6. This results in a    smaller ellipse, which typically describes the “gravity” center of    the ellipses.-   b) As the shrinking process of step a) is only based on geometric    criteria, it is possible that important sub-areas, having a high    location probability, are removed. To take these important sub-areas    into account, small clouds are generated from sub-areas having a    location probability twice the mean probability of the respective    sub cloud. Convex envelopes and polygons are constructed around    these small clouds. It is possible that these small clouds protrude    from the ellipse from step a).-   c) A final convex envelope is constructed around the ellipse from    step a) and the sub-areas of the polygons from step b). The ellipse    is approximated in this step with a number of typical points of the    ellipse. Around the envelope, the final ellipse is fit which    contains the shrunken ellipse from a) but also the best points of    the sub cloud from b).

In step S54, the location area determination module 37 checks if thenumber of ellipses 5 exceeds a defined (configurable) maximum number ofellipses, e.g. 10. In this case the method continues in step S55;otherwise, the method continues in step S62.

In step S55, the location area determination module 37 checks if thetarget probability has reached 100%. In this case the method continuesin step S657; otherwise, the method continues in step S56.

In step S56, the location area determination module 37 increases thetarget probability by a defined (configurable) incremental value, e.g.5%, and continues in step S53.

Although the method may seem complex and difficult to implement, it hasa significant advantage: it requires much less processing time than manyother methods. For a cloud including n sub-areas, the time needed tocomplete the calculation is of the order of O(n*log(n)). On aconventional PC, a cloud of 15,000 sub-areas is processed in less than10 minutes.

In step S57, the location area determination module 37 uses an improvedbut more complex approach to determine more than one ellipse to definethe location area. The main idea is the same as in step S53, with thedifference not to search for direct connected sub-areas but also forsub-areas which are more away from the ellipses already created,checking for each of them if there is enough probability covered in thearea between them and the nearest ellipse. All the sub-areas aretherefore in a first step sorted in a list by their decreasing locationprobabilities. Starting with the first sub-area of the list, a newellipse is created at the location of the sub-area. In descending orderof their location probabilities, it is checked whether the next sub-areacan be used to extend its nearest ellipse. It does extend the nearestellipse, if it is separated from the nearest ellipse by an intermediatearea 8 having at least a defined (configurable) minimum mean locationprobability, i.e. an intermediate area, e.g. a circle, comprisingsub-areas with at least a minimum average location probability. If thesub-area does not match the criteria, a new ellipse is created aroundthis sub-area as long as a defined (configurable) maximum number ofellipses has not been reached. If the maximum has been reached thesub-area is ignored and does not influence the construction of theellipses. As the sub-areas are processed according to their locationprobability, this happens typically to sub-areas which do have a lowlocation probability and are therefore less important than sub-areasfrom the beginning of the list. The whole procedure is repeated for allsub-areas of the list until all are processed once, or the sum of thelocation probabilities of the sub-areas which were not ignored reaches adefined (configurable) minimum target probability.

In step S58, the location area determination module 37 checks if thedefined (configurable) minimum target probability has been reached. Ifthis is the case, the method continues in step S62; otherwise, themethod continues in step S59.

As illustrated in FIG. 7, as soon as there are in step S57 more than oneellipses E1, E2, the location area determination module 37 checks instep S57 for the remaining sub-areas 9, in descending order of theirlocation probabilities, whether the sub-areas 9 are to be included inthe ellipse E2 located closest to its location. In the example shown inFIG. 7, checked is whether the sub-areas included in intermediate area8, which separates sub-area 9 from its closest ellipse E2 (distance D2is shorter than distance D1), have at least a defined (configurable)minimum mean location probability.

In step S59, as the desired defined (configurable) minimum targetprobability has not been reached, the location area determination module37 determines whether the threshold for the minimum mean locationprobability of the intermediate area between the respective sub-area andits closest ellipse has reached a value below a defined (configurable)minimum probability. If this is the case, the method continues in stepS61; otherwise, the method continues in step S60 to prepare a next trialwith a lower threshold.

In step S60, the location area determination module 37 lowers theminimum mean location probability for the intermediate area by a defined(configurable) amount, e.g. by 75%, and continues in step S57 to start anew approach to describe the distribution of location probability withmultiple ellipses.

In Step S61, the location area determination module 37 generates awarning indicating to an operator that there was no method which coulddescribe successfully in an optimal way the user location area throughellipses. However the last set of ellipses generated in step S57 is usedfor output and the method continues in step S62.

In step S62, the location area determination module 37 generates anoutput signal with a graph showing the location area with one or moreellipses. For example, the ellipse(s) associated with the determinedlocation area are shown on a display, printed on paper, send in amessage or included in an image file.

The first approach, described in the context of steps S50-S52, and thesecond approach, described in the context of steps S53-S56, requireprocessing times in the order of O(n*log(n)), for n sub-areas. The thirdapproach requires a processing time in the order of O(n2). While thethird approach is significantly slower than the first and secondapproach, it is a useful and necessary fallback strategy forcompensating shortcomings of the previous approaches. Practical resultsfrom real life data have shown the first approach is used in 11% of allcases; the second approach is used in 88%; while the third approach isused in 1% of all cases.

1-13. (canceled)
 14. A method, comprising: determining antennaprobabilities for a plurality of antennas in an area covered by a mobileradio network; and determining a location area for a user using a mobilecommunication terminal operating in the mobile radio network based onthe antenna probabilities.